Explainable artificial intelligence for feature selection in network traffic classification: A comparative study

Over the past decade, there has been a growing surge of interest in leveraging artificial intelligence and machine learning models to address real‐world challenges within the field of telecommunications and networking. Among these challenges, network traffic classification has consistently remained a key focal point within the community. The emergence of new paradigms such as internet of things has added complexity to this challenge, owing to the diverse array of traffic classes it introduces. This complexity is further compounded when dealing with encrypted traffic data. In the context of this paper, we employ explainable artificial intelligence (XAI) techniques to offer insightful explanations for the task of classifying encrypted network traffic. Our approach involves presenting a ranked list of the model's input features, organized based on their significance in influencing the classifier's predictions. These ranked lists shed light on the underlying functionality of the encrypted traffic classification model, enabling users to discern which features hold greater importance in shaping the model's output by harnessing the insights provided by XAI approaches. Moreover, we carry out a quantitative assessment and comparison of the results generated by these XAI methods and outputs of three other conventional, widely adopted feature selection approaches, utilizing them as feature selection techniques when applied to a convolutional neural network for classifying data from the CIC‐Darknet2020 and CIC‐IDS2017 datasets. The results show that XAI techniques have high performance in feature selection for network traffic classification. In the context of this paper, we employ explainable artificial intelligence techniques to offer insightful explanations for the task of classifying encrypted network traffic. Our approach involves presenting a ranked list of the model's input features, organized based on their significance in influencing the classifier's predictions.